We first sample the new images \(\tilde{x}_v\) , which have the same text labels with given audio \(x_a\) . Then, we use the image encoder to obtain a new sampled image embedding \(\tilde{v} \in \mathbb {R}^d\) . We apply the knowledge distillation technique [1]} to transfer the generalization ability of the CLIP to sound representation. The learning scheme allows the similarity matrix between audio-visual and visual-text to be equal in mini-batch (see Fig. REF ).
